AI NEWS.ccAI is happening. The future is updating.
♡ Saved tools 0
AI NEWS · Model intelligenceExplore capabilities. Make informed choices. ↗Profiles indexed first · Details continuously enriched
THE AI MODEL DIRECTORY Start exploring models here

Find your ideal AI model.

From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.

Popular searches
84,991 model profiles13202 providers / publishers45 capability categories465 with comparable prices · 30 benchmarked
TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
DISCOVER & COMPARE

Explore AI models 2043

Discover models worth your attention

Octen-Embedding-0.6B is a text embedding model developed by Octen for semantic search and retrieval tasks. This model is fine-tuned from Qwen/Qwen3-Embedding-0.6B and supports multiple languages, providing high-quality embeddings for various applications. **Model Family Design**: - **Octen-Embedding-8B**: Best performance, RTEB #1, for high-precision retrieval - **Octen-Embedding-4B**: Best in 4B category, balanced performance and efficiency - **Octen-Embedding-0.6B**: Lightweight deployment, suitable for edge devices and resource-constrained environments This model is derived from Qwen/Qwen3-Embedding-0.6B, which is also licensed under Apache License 2.0.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

Octen-Embedding-8B is a text embedding model developed by Octen for semantic search and retrieval tasks. This model is fine-tuned from Qwen/Qwen3-Embedding-8B and supports multiple languages, providing high-quality embeddings for various applications. **Model Family Design**: - **Octen-Embedding-8B**: Best performance, RTEB #1, for high-precision retrieval - **Octen-Embedding-4B**: Best in 4B category, balanced performance and efficiency - **Octen-Embedding-0.6B**: Lightweight deployment, suitable for edge devices and resource-constrained environments When encoding documents without any instruction prefix, you may encounter unexpected behavior due to an upstream issue in Qwen3-Embedding. To avoid this issue, we recommend adding `"- "` (dash followed by space) at the beginning of your text when encoding documents:

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

🚀 🚀 This is **Arabic only** sentence-transformers model finetuned from aubmindlab/bert-base-arabertv02. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for **semantic textual similarity**, **semantic search**, **clustering**, and more. This model supports Matryoshka embeddings, allowing you to truncate embeddings into smaller sizes to optimize performance and memory usage, based on your task requirements. Available truncation sizes include: **768**, **512**, **256**, **128**, and **64** You can select the appropriate embedding size for your use case, ensuring flexibility in resource management.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

Arabic-Triplet-Matryoshka-V2

Omartificial-Intelligence-Space

ATM2 is a **135M-parameter Arabic embedding model** fine-tuned from AraBERTv02 using the Sentence Transformers framework. It maps Arabic sentences and passages into **768-dimensional vectors** for comparing meaning and retrieving related content. ATM2 produces embeddings, not generated answers. Classification requires an additional classifier or decision rule; question answering requires downstream retrieval and answer-generation components. For larger collections, precompute passage embeddings and store them in a vector index. Split long documents into passages within the effective sequence limit. Similarity scores indicate relative semantic proximity; they are not calibrated probabilities of correctness.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

GATE-AraBert-v1

Omartificial-Intelligence-Space

This is **GATE | General Arabic Text Embedding** trained using SentenceTransformers in a **multi-task** setup. The system trains on the **AllNLI** and on the **STS** dataset. It is described in detail in the paper GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Hybrid Loss Training. **Project page:** https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e The author would like to thank Prince Sultan University for their invaluable support in this project. Their contributions and resources have been instrumental in the development and fine-tuning of these models.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

all-MiniLM-L6-v2-ONNX

onnx-community

https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

Understand before you choose

Selection guide & FAQ
How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

AI NEWS · From information to decisions

Follow trends. Find tools. Explore models.

Explore AI news ↗Discover AI tools ↗